Abstract
This paper considers the problem of state estimation with unknown process and measurement noise covariance for a linear Gaussian system. Under the assumption of conjugate priors, inverse Wishart distribution is chosen for both process and measurement noise covariance by introducing a latent variable. Then, the joint state estimation and unknown parameters identification is derived in variational Bayesian framework, and the system state, latent variable, process and measurement noise covariance are updated iteratively. The performance of the proposed algorithm is demonstrated by comparing with the other VB based adaptive filter in a target tracking simulation system.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
